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Identification of difficult laryngoscopy using an optimized hybrid architecture
Date
2024
Abstract
Background identification of difficult laryngoscopy is a frequent demand in cervical spondylosis clinical surgery. This work aims to develop a hybrid architecture for identifying difficult laryngoscopy based on new indexes. Methods Initially, two new indexes for identifying difficult laryngoscopy are proposed, and their efficacy for predicting difficult laryngoscopy is compared to that of two conventional indexes. Second, a hybrid adaptive architecture with convolutional layers, spatial extraction, and a vision transformer is proposed for predicting difficult laryngoscopy. The proposed adaptive hybrid architecture is then optimized by determining the optimal location for extracting spatial information. Results The test accuracy of four indexes using simple model is 0.8320. The test accuracy of optimized hybrid architecture using four indexes is 0.8482. Conclusion The newly proposed two indexes, the angle between the lower margins of the second and sixth cervical spines and the vertical direction, are validated to be effective for recognizing difficult laryngoscopy. In addition, the optimized hybrid architecture employing four indexes demonstrates improved efficacy in detecting difficult laryngoscopy. Trial registration Ethics permission for this research was obtained from the Medical Scientific Research Ethics Committee of Peking University Third Hospital (IRB00006761-2015021) on 30 March 2015. A well-informed agreement has been received from all participants. Patients were enrolled in this research at the Chinese Clinical Trial Registry (http://www.chictr.org.cn, identifier: ChiCTR-ROC-16008598) on 6 June 2016
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Publisher
BMC
Citation
BMC Medical Research Methodology 24, 4
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Funding Information
Wu Jieping Medical Foundation (320.6750.2023-08-5), Key Clinical Projects of Peking University Third Hospital (BYSYZD2021013), Innovation and Transformation Project, Peking University Third Hospital (BYSYZHKC2022103), Clinical Medicine Plus X - Young Scholars Project from Peking University (PKU2022LCXQ031)